AutoCTS: Automated Correlated Time Series Forecasting
Summary: AutoCTS automates correlated time series forecasting by jointly searching micro-level ST-blocks and macro-level topologies. It evolves heterogeneous ST-block architectures and diverse connections, outperforming state-of-the-art human-designed models on eight CTS benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinle Wu (Aalborg University)
- 2. Dalin Zhang (Aalborg University)
- 3. Chenjuan Guo (Aalborg University)
- 4. Chaoyang He (University of Southern California)
- 5. Bin Yang (Aalborg University)
- 6. Christian S. Jensen (Aalborg University)
BibTeX Citation
@article{wu_vldb22,
title = {{AutoCTS: Automated Correlated Time Series Forecasting}},
author = {Wu, Xinle and Zhang, Dalin and Guo, Chenjuan and He, Chaoyang and Yang, Bin and Jensen, Christian S.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {4},
pages = {971--983},
doi = {10.14778/3503585.3503604},
url = {https://doi.org/10.14778/3503585.3503604},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,272 | VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition | 2021 | VLDB | 7.5775321e-05 |
| 3,694 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 7.1937882e-05 |
| 4,851 | Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles | 2022 | VLDB | 6.4803317e-05 |
| 5,839 | Doing More with Less: Characterizing Dataset Downsampling for AutoML | 2021 | VLDB | 6.0699037e-05 |
| 8,541 | Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models | 2013 | VLDB | 5.4119882e-05 |
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